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  • Article  (9)
  • computing and processing
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  • Article  (9)
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  • 1
    Language: English
    In: IEEE Transactions on Knowledge and Data Engineering, 01 November 2016, Vol.28(11), pp.2884-2894
    Description: In this study, we propose a novel vector quantization algorithm for Approximate Nearest Neighbor (ANN) search, based on a joint competitive learning strategy and hence called as competitive quantization (CompQ). CompQ is a hierarchical algorithm, which iteratively minimizes the quantization error by jointly optimizing the codebooks in each layer, using a gradient decent approach. An extensive set of experimental results and comparative evaluations show that CompQ outperforms the-state-of-the-art while retaining a comparable computational complexity.
    Keywords: Vector Quantization ; Encoding ; Optimization ; Hamming Distance ; Electronic Mail ; Nearest Neighbor Searches ; Approximate Nearest Neighbor Search ; Binary Codes ; Large-Scale Retrieval ; Vector Quantization ; Engineering ; Computer Science
    ISSN: 1041-4347
    E-ISSN: 1558-2191
    Source: IEEE Conference Publications
    Source: IEEE Journals & Magazines 
    Source: IEEE Xplore
    Source: IEEE Journals & Magazines
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  • 2
    Language: English
    In: IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), August 2012, Vol.42(4), pp.1169-1186
    Description: Terrain classification over polarimetric synthetic aperture radar (SAR) images has been an active research field where several features and classifiers have been proposed up to date. However, some key questions, e.g., 1) how to select certain features so as to achieve highest discrimination over certain classes?, 2) how to combine them in the most effective way?, 3) which distance metric to apply?, 4) how to find the optimal classifier configuration for the classification problem in hand?, 5) how to scale/adapt the classifier if large number of classes/features are present?, and finally, 6) how to train the classifier efficiently to maximize the classification accuracy?, still remain unanswered. In this paper, we propose a collective network of (evolutionary) binary classifier (CNBC) framework to address all these problems and to achieve high classification performance. The CNBC framework adapts a "Divide and Conquer" type approach by allocating several NBCs to discriminate each class and performs evolutionary search to find the optimal BC in each NBC. In such an (incremental) evolution session, the CNBC body can further dynamically adapt itself with each new incoming class/feature set without a full-scale retraining or reconfiguration. Both visual and numerical performance evaluations of the proposed framework over two benchmark SAR images demonstrate its superiority and a significant performance gap against several major classifiers in this field.
    Keywords: Neurons ; Scattering ; Covariance Matrix ; Training ; Matrix Decomposition ; Indexes ; Measurement ; Evolutionary Classifiers ; Multidimensional Particle Swarm Optimization (MD-Pso) ; Polarimetric Synthetic Aperture Radar (SAR) ; Sciences (General) ; Engineering
    ISSN: 1083-4419
    E-ISSN: 1941-0492
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  • 3
    Language: English
    In: IEEE Transactions on Knowledge and Data Engineering, 01 July 2016, Vol.28(7), pp.1722-1733
    Description: Approximate Nearest Neighbor (ANN) search has become a popular approach for performing fast and efficient retrieval on very large-scale datasets in recent years, as the size and dimension of data grow continuously. In this paper, we propose a novel vector quantization method for ANN search which enables faster and more accurate retrieval on publicly available datasets. We define vector quantization as a multiple affine subspace learning problem and explore the quantization centroids on multiple affine subspaces. We propose an iterative approach to minimize the quantization error in order to create a novel quantization scheme, which outperforms the state-of-the-art algorithms. The computational cost of our method is also comparable to that of the competing methods.
    Keywords: Principal Component Analysis ; Artificial Neural Networks ; Vector Quantization ; Iterative Methods ; Encoding ; Nearest Neighbor Searches ; Approximate Nearest Neighbor Search ; Binary Codes ; Large-Scale Retrieval ; Subspace Clustering ; Vector Quantization ; Engineering ; Computer Science
    ISSN: 1041-4347
    E-ISSN: 1558-2191
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  • 4
    Language: English
    In: IEEE Transactions on Neural Networks and Learning Systems, December 2016, Vol.27(12), pp.2458-2471
    Description: In training radial basis function neural networks (RBFNNs), the locations of Gaussian neurons are commonly determined by clustering. Training inputs can be clustered on a fully unsupervised manner (input clustering), or some supervision can be introduced, for example, by concatenating the input vectors with weighted output vectors (input-output clustering). In this paper, we propose to apply clustering separately for each class (class-specific clustering). The idea has been used in some previous works, but without evaluating the benefits of the approach. We compare the class-specific, input, and input-output clustering approaches in terms of classification performance and computational efficiency when training RBFNNs. To accomplish this objective, we apply three different clustering algorithms and conduct experiments on 25 benchmark data sets. We show that the class-specific approach significantly reduces the overall complexity of the clustering, and our experimental results demonstrate that it can also lead to a significant gain in the classification performance, especially for the networks with a relatively few Gaussian neurons. Among other applied clustering algorithms, we combine, for the first time, a dynamic evolutionary optimization method, multidimensional particle swarm optimization, and the class-specific clustering to optimize the number of cluster centroids and their locations.
    Keywords: Training ; Neurons ; Clustering Algorithms ; Optimization ; Neural Networks ; Heuristic Algorithms ; Particle Swarm Optimization ; Clustering Methods ; Particle Swarm Optimization (Pso) ; Radial Basis Function Networks (Rbfnns) ; Supervised Learning ; Computer Science
    ISSN: 2162-237X
    E-ISSN: 2162-2388
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  • 5
    Language: English
    In: IEEE Transactions on Neural Networks and Learning Systems, 31 May 2019, pp.1-15
    Description: The traditional multilayer perceptron (MLP) using a McCulloch-Pitts neuron model is inherently limited to a set of neuronal activities, i.e., linear weighted sum followed by nonlinear thresholding step. Previously, generalized operational perceptron (GOP) was proposed to extend the conventional perceptron model by defining a diverse set of neuronal activities to imitate a generalized model of biological neurons. Together with GOP, a progressive operational perceptron (POP) algorithm was proposed to optimize a predefined template of multiple homogeneous layers in a layerwise manner. In this paper, we propose an efficient algorithm to learn a compact, fully heterogeneous multilayer network that allows each individual neuron, regardless of the layer, to have distinct characteristics. Based on the complexity of the problem, the proposed algorithm operates in a progressive manner on a neuronal level, searching for a compact topology, not only in terms of depth but also width, i.e., the number of neurons in each layer. The proposed algorithm is shown to outperform other related learning methods in extensive experiments on several classification problems.
    Keywords: Neurons ; Biological Neural Networks ; Network Topology ; Learning Systems ; Topology ; Computational Modeling ; Nonhomogeneous Media ; Architecture Learning ; Feedforward Network ; Generalized Operational Perceptron (GOP) ; Progressive Learning ; Computer Science
    ISSN: 2162-237X
    E-ISSN: 2162-2388
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  • 6
    Language: English
    In: IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society, April 2010, Vol.40(2), pp.298-319
    Description: In this paper, we propose two novel techniques, which successfully address several major problems in the field of particle swarm optimization (PSO) and promise a significant breakthrough over complex multimodal optimization problems at high dimensions. The first one, which is the so-called multidimensional (MD) PSO, re-forms the native structure of swarm particles in such a way that they can make interdimensional passes with a dedicated dimensional PSO process. Therefore, in an MD search space, where the optimum dimension is unknown, swarm particles can seek both positional and dimensional optima. This eventually removes the necessity of setting a fixed dimension a priori, which is a common drawback for the family of swarm optimizers. Nevertheless, MD PSO is still susceptible to premature convergences due to lack of divergence. Among many PSO variants in the literature, none yields a robust solution, particularly over multimodal complex problems at high dimensions. To address this problem, we propose the fractional global best formation (FGBF) technique, which basically collects all the best dimensional components and fractionally creates an artificial global best (aGB) particle that has the potential to be a better "guide" than the PSO's native gbest particle. This way, the potential diversity that is present among the dimensions of swarm particles can be efficiently used within the aGB particle. We investigated both individual and mutual applications of the proposed techniques over the following two well-known domains: 1) nonlinear function minimization and 2) data clustering. An extensive set of experiments shows that in both application domains, MD PSO with FGBF exhibits an impressive speed gain and converges to the global optima at the true dimension regardless of the search space dimension, swarm size, and the complexity of the problem.
    Keywords: Particle Swarm Optimization ; Multidimensional Systems ; Organisms ; Genetic Programming ; Particle Tracking ; Robustness ; Stochastic Processes ; Multidimensional Signal Processing ; Computer Simulation ; Fractional Global Best Formation (Fgbf) ; Multidimensional (MD) Search ; Particle Swarm Optimization (Pso) ; Sciences (General) ; Engineering;
    ISSN: 10834419
    E-ISSN: 1941-0492
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  • 7
    Language: English
    In: IEEE Transactions on Neural Systems and Rehabilitation Engineering, March 2016, Vol.24(3), pp.386-398
    Description: In this paper, the performance of the phase space representation in interpreting the underlying dynamics of epileptic seizures is investigated and a novel patient-specific seizure detection approach is proposed based on the dynamics of EEG signals. To accomplish this, the trajectories of seizure and nonseizure segments are reconstructed in a high dimensional space using time-delay embedding method. Afterwards, Principal Component Analysis (PCA) was used in order to reduce the dimension of the reconstructed phase spaces. The geometry of the trajectories in the lower dimensions is then characterized using Poincaré section and seven features were extracted from the obtained intersection sequence. Once the features are formed, they are fed into a two-layer classification scheme, comprising the Linear Discriminant Analysis (LDA) and Naive Bayesian classifiers. The performance of the proposed method is then evaluated over the CHB-MIT benchmark database and the proposed approach achieved 88.27% sensitivity and 93.21% specificity on average with 25% training data. Finally, we perform comparative performance evaluations against the state-of-the-art methods in this domain which demonstrate the superiority of the proposed method.
    Keywords: Electroencephalography ; Feature Extraction ; Trajectory ; Nonlinear Dynamical Systems ; Epilepsy ; Geometry ; Benchmark Testing ; Dynamics ; Electroencephalography (EEG) ; Phase Space ; Poincaré Section ; Seizure Detection ; Two-Layer Classifier Topology ; Occupational Therapy & Rehabilitation
    ISSN: 1534-4320
    E-ISSN: 1558-0210
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  • 8
    Language: English
    In: IEEE Journal of Biomedical and Health Informatics, 26 March 2019, pp.1-1
    Description: Nonlinear dynamics has recently been extensively used to study epilepsy due to the complex nature of the neuronal systems. This study presents a novel method that characterizes the dynamic behavior of pediatric seizure events and introduces a systematic approach to locate the nullclines on the phase space when the governing differential equations are unknown. Nullclines represent the locus of points in the solution space where the components of the velocity vectors are zero. A simulation study over 5 benchmark nonlinear systems with well-known differential equations in 3D exhibits the characterization efficiency and accuracy of the proposed approach that is solely based on the reconstructed solution trajectory. Due to their unique characteristics in the nonlinear dynamics of epilepsy, discriminative features can be extracted based on the nullclines concept. Using a limited training data (only 25% of each EEG record) in order to mimic the real-world clinical practice, the proposed approach achieves 91.15% average sensitivity and 95.16% average specificity over the benchmark CHB-MIT dataset. Together with an elegant computational efficiency, the proposed approach can, therefore, be an automatic and reliable solution for patient-specific seizure detection in long EEG recordings.
    Keywords: Differential Equations ; Feature Extraction ; Nonlinear Dynamical Systems ; Trajectory ; Electroencephalography ; Stability Analysis ; Systematics ; EEG ; Seizure Detection ; Nonlinear Dynamics ; Phase Space ; Nullcline ; Lda ; Ann ; Medicine
    ISSN: 2168-2194
    E-ISSN: 2168-2208
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  • 9
    Language: English
    In: IEEE Access, 2019, Vol.7, pp.67305-67318
    Description: The use of unmanned aerial vehicles (UAVs) in future wireless networks is gaining attention due to their quick deployment without requiring the existing infrastructure. Earlier studies on UAV-aided communication consider generic scenarios, and very few studies exist on the evaluation of UAV-aided communication in practical networks. The existing studies also have several limitations, and hence, an extensive evaluation of the benefits of UAV communication in practical networks is needed. In this paper, we proposed a UAV-aided Wi-Fi Direct network architecture. In the proposed architecture, a UAV equipped with a Wi-Fi Direct group owner (GO) device, the so-called Soft-AP, is deployed in the network to serve a set of Wi-Fi stations. We propose to use a simpler yet efficient algorithm for the optimal placement of the UAV. The proposed algorithm dynamically places the UAV in the network to reduce the distance between the GO and client devices. The expected benefits of the proposed scheme are to maintain the connectivity of client devices to increase the overall network throughput and to improve energy efficiency. As a proof of concept, realistic simulations are performed in the NS-3 network simulator to validate the claimed benefits of the proposed scheme. The simulation results report major improvements of 23% in client association, 54% in network throughput, and 33% in energy consumption using single UAV relative to the case of stationary or randomly moving GO. Further improvements are achieved by increasing the number of UAVs in the network. To the best of our knowledge, no prior work exists on the evaluation of the UAV-aided Wi-Fi Direct networks.
    Keywords: Wireless Fidelity ; Throughput ; Network Architecture ; Unmanned Aerial Vehicles ; Communication Networks ; Base Stations ; Spread Spectrum Communication ; Access Point ; Group Owner ; Ns-3 ; Unmanned Aerial Vehicle ; Wi-Fi Direct ; Engineering
    E-ISSN: 2169-3536
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